Archives
Current issue
About
About us
Aims and Scope
Abstracting and Indexing
Editorial Office
Open Access Policy
Publication Ethics
Journal History
Publisher
Follow us: Twitter
Contact
For Authors
Editorial Policy
Peer Review Policy
Manuscript Preparation Guidelines
Copyright & Licencing
Publication Fees
Fee Waiver Policy for Doctoral Students
EJMSTE Language Editing Service
More
Statistics
Special Issues
Special Issue Announcements
Published Special Issues
Disaster Recovery Site Evaluations and Selections for Information Systems of Academic Big Data
1
National Center for High-Performance Computing, Hsinchu, Taiwan
2
Department of Industrial Education, National Taiwan Normal University, Taiwan
3
Department of Technology Management for Innovation, Graduate School of Engineering
The University of Tokyo, Japan
4
Institute of Computer and Communication Engineering, National Cheng Kung University, Taiwan
Online publication date: 2017-07-27
Publication date: 2017-07-27
Corresponding author
Chi-Yo Huang
Department of Industrial Education, National Taiwan Normal University, No. 129, Heping East Road 1, 10610 Taipei, Taiwan
Department of Industrial Education, National Taiwan Normal University, No. 129, Heping East Road 1, 10610 Taipei, Taiwan
EURASIA J. Math., Sci Tech. Ed 2017;13(8):4553-4589
KEYWORDS
site selectionbig dataDisaster recovery (DR)DEMATEL-based Network Process (DNP)Multiple Criteria Decision Making (MCDM)
TOPICS
ABSTRACT
The most dramatic factor shaping the future of higher education is Big Data and analytics. In the Big Data era, the explosive growth of massive data manipulations imposes a heavy burden on computation, storage, and communication in data centers. Increasing uncertainties in information system availability have become a daily serious problem. An appropriate evaluation and selection of the right information system disaster recovery (DR) site can ensure business continuity and investment optimization. Therefore, this research aims to establish an analytic framework for evaluating, selecting DR sites for academic Big Data. The proposed analytic framework is consisting of the Decision-Making Trial and Evaluation Laboratory (DEMATEL), DEMATEL-based network process (DNP) and VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) methods. An empirical study based on a real Big Data DR application of an Asian high-performance computer center’s evaluation and selection of DR sites for academic Big Data will be used to illustrate the feasibility of the proposed framework. The analytic results can serve as a foundation for information technology (IT) administrators’ strategies to reduce the performance gaps of a DR site for Big Data manipulations in general, and academic Big Data manipulations in special.
REFERENCES (122)
1.
Ablanedo-Rosas, J. H., Gao, H., Alidaee, B., & Teng, W.-Y. (2009). Allocation of emergency and recovery centres in Hidalgo, Mexico. International Journal of Services Sciences, 2(2), 206-218. doi:10.1504/IJSSCI.2009.024941.
2.
Al-Shaikh, R., Al-Hussain, Z., & Al-Sharidah, A. (2015). Toward Building an IT Disaster Recovery Site for Oil and Gas Companies. Paper presented at the Computational Intelligence, Communication Systems and Networks (CICSyN), 2015 7th International Conference on. doi:10.1109/CICSyN.2015.39.
3.
Alesi, P. (2008). Building enterprise-wide resilience by integrating business continuity capability into day-to-day business culture and technology. Journal of Business Continuity & Emergency Planning, 2(3), 214-220.
4.
Alhazmi, O. H., & Malaiya, Y. K. (2012). Assessing disaster recovery alternatives: On-site, colocation or cloud. Paper presented at the Software Reliability Engineering Workshops (ISSREW), 2012 IEEE 23rd International Symposium on. doi:10.1109/ISSREW.2012.20.
5.
Altay, N., & Green, W. G. (2006). OR/MS research in disaster operations management. European Journal of Operational Research, 175(1), 475-493.
6.
Andreolini, M., Colajanni, M., Pietri, M., & Tosi, S. (2015). Adaptive, scalable and reliable monitoring of big data on clouds. Journal of Parallel and Distributed Computing, 79, 67-79. doi:10.1016/j.jpdc.2014.08.007.
7.
Anthopoulos, L. G., Kostavara, E., & Pantouvakis, J.-P. (2013). An effective disaster recovery model for construction projects. Procedia-Social and Behavioral Sciences, 74, 21-30. doi:10.1016/j.sbspro.2013.03.026.
8.
Awasthi, A., Chauhan, S. S., & Goyal, S. K. (2011). A multi-criteria decision making approach for location planning for urban distribution centers under uncertainty. Mathematical and Computer Modelling, 53(1), 98-109. doi:10.1016/j.mcm.2010.07.023.
9.
Bahrami, M., & Singhal, M. (2015). The role of cloud computing architecture in big data. Information granularity, big data, and computational intelligence (pp. 275-295): Springer. doi:10.1007/978-3-319-08254-7_13.
10.
Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. JEDM-Journal of Educational Data Mining, 1(1), 3-17.
11.
Balcik, B., & Beamon, B. M. (2008). Facility location in humanitarian relief. International Journal of Logistics, 11(2), 101-121. doi:10.1080/13675560701561789.
12.
Beggan, D. M. (2011). Disaster recovery considerations for academic institutions. Disaster Prevention and Management: An International Journal, 20(4), 413-422. doi:10.1108/09653561111161734.
13.
Berman, J. J. (2013). Principles of big data: preparing, sharing, and analyzing complex information: Newnes.
14.
Bertrand, C. (2005). Business continuity and mission critical applications. Network Security, 2005(8), 9-11. doi:10.1016/S1353-4858(05)70269-5.
15.
Beyer, M. A., & Laney, D. (2012). The importance of ‘big data’: a definition. Stamford, CT: Gartner, 2014-2018.
16.
Bowen, P., Hash, J., & Wilson, M. (2006). SP 800-100. Information Security Handbook: A Guide for Managers: NIST.
17.
Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15(5), 662-679. doi:10.1080/1369118X.2012.678878.
18.
Broder, J. F., & Tucker, E. (2011). Risk analysis and the security survey: Elsevier.
19.
Brooks, C., Bedernjak, M., Juran, I., & Merryman, J. (2002). Disaster Recovery Strategies. IBM Redbook.
20.
BS25999. (2007). Business Continuity Management-Part2: Specification Business Continuity Management., from http://www.25999.info/bs25999-....
21.
Cegiela, R. (2006). Selecting technology for disaster recovery. Paper presented at the 2006 International Conference on Dependability of Computer Systems. 160 - 167. doi:10.1109/DEPCOS-RELCOMEX.2006.49.
22.
Cerullo, V., & Cerullo, M. J. (2004). Business continuity planning: a comprehensive approach. Information Systems Management, 21(3), 70-78. DOI:10.1201/1078/44432.21.3.20040601/82480.11.
23.
Chang, R. M., Kauffman, R. J., & Kwon, Y. (2014). Understanding the paradigm shift to computational social science in the presence of big data. Decision Support Systems, 63, 67-80. doi:10.1016/j.dss.2013.08.008.
24.
Chang, V. (2015). Towards a Big Data system disaster recovery in a Private Cloud. Ad Hoc Networks, 35, 65-82. doi:10.1016/j.adhoc.2015.07.012.
25.
Chang, Y., Wilkinson, S., Potangaroa, R., & Seville, E. (2012). Managing resources in disaster recovery projects. Engineering, Construction and Architectural Management, 19(5), 557-580. doi:10.1108/09699981211259621.
26.
Chen, C. P., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347. doi:10.1016/j.ins.2014.01.015.
27.
Chen, R., Sharman, R., Rao, H. R., & Upadhyaya, S. J. (2008). Coordination in emergency response management. Communications of the ACM, 51(5), 66-73.
28.
Claunch, C. (2004). Management update: best practices in business continuity and disaster recovery. Gartner Research.
29.
Clitherow, D., Brookbanks, M., Clayton, N., & Spear, G. (2008). Combining high availability and disaster recovery solutions for critical IT environments. IBM Systems Journal, 47(4), 563. doi:10.1147/SJ.2008.5386509.
30.
Computing Research Association. (2015). Data-Intensive Research in Education: Current Work and Next Steps. Washington, D.C.: Computing Research Association.
31.
Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management science, 9(3), 458-467. doi:10.1287/mnsc.9.3.458.
32.
Dawes, S. S., Cresswell, A. M., & Cahan, B. B. (2004). Learning from crisis lessons in human and information infrastructure from the world trade center response. Social Science Computer Review, 22(1), 52-66. doi:10.1177/0894439303259887.
33.
Eynon, R. (2013). The rise of Big Data: what does it mean for education, technology, and media research? Learning, Media and Technology, 38(3), 237-240. doi:10.1080/17439884.2013.771783.
34.
Fan, Z.-P., Suo, W.-L., & Feng, B. (2012). Identifying risk factors of IT outsourcing using interdependent information: An extended DEMATEL method. Expert systems with Applications, 39(3), 3832-3840. doi:10.1016/j.eswa.2011.09.092.
35.
Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5-6), 304-317. doi:10.1504/IJTEL.2012.051816.
36.
Fontela, E., & Gabus, A. (1976). The DEMATEL observer: Dematel 1976 Report. Switzerland Geneva, Battelle Geneva Research Center.
37.
Gallego, D., & Bueno, S. (2014). Exploring the application of the Delphi method as a forecasting tool in Information Systems and Technologies research. Technology Analysis & Strategic Management, 26(9), 987-999. doi:10.1080/09537325.2014.941348.
38.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144. doi:10.1016/j.ijinfomgt.2014.10.007.
40.
Gantz, J., & Reinsel, D. (2012). The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east. IDC iView: IDC Analyze the Future, 2007, 1-16.
41.
Garlasu, D., Sandulescu, V., Halcu, I., Neculoiu, G., Grigoriu, O., Marinescu, M., & Marinescu, V. (2013). A big data implementation based on Grid computing. Paper presented at the Roedunet International Conference (RoEduNet), 2013 11th.1-4. doi:10.1109/RoEduNet.2013.6511732.
42.
Gibb, F., & Buchanan, S. (2006). A framework for business continuity management. International journal of information management, 26(2), 128-141. doi:10.1016/j.ijinfomgt.2005.11.008.
43.
Greenberg, A., Hamilton, J. R., Jain, N., Kandula, S., Kim, C., Lahiri, P., Sengupta, S. (2009). VL2: a scalable and flexible data center network. Paper presented at the ACM SIGCOMM computer communication review.
44.
Gregory, P. H. (2011). IT disaster recovery planning for dummies: John Wiley & Sons.
45.
Zeng, D., Gu, L., & Guo, S. (2015). Cost minimization for big data processing in geo-distributed data centers. In Cloud Networking for Big Data (pp. 59-78). Springer. doi:10.1007/978-3-319-24720-5_4.
46.
Han, H., Li, L., & Zhu, D. (2012). Research and Implementation on Remote Disaster Recovery System. Paper presented at the Computer Science & Service System (CSSS), 2012 International Conference on. 875 - 879. doi:10.1109/CSSS.2012.223.
47.
Hanaoka, S., & Qadir, F. M. (2005). Logistics problems in recovery assistance of Indian Ocean earthquake and tsunami disaster. Paper presented at the Scientific Forum on The Tsunami, Its Impact and Recovery.
48.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98-115. doi:10.1016/j.is.2014.07.006.
49.
Herbane, B., Elliott, D., & Swartz, E. M. (2004). Business continuity management: time for a strategic role? Long Range Planning, 37(5), 435-457. doi:10.1016/j.lrp.2004.07.010.
50.
Hsieh, Y.-F., Lee, Y.-C., & Lin, S.-B. (2016). Rebuilding DEMATEL threshold value: an example of a food and beverage information system. SpringerPlus, 5(1), 1385. doi:10.1186/s40064-016-3083-7.
51.
Hsu, C.-W., Kuo, T.-C., Chen, S.-H., & Hu, A. H. (2013). Using DEMATEL to develop a carbon management model of supplier selection in green supply chain management. Journal of cleaner production, 56, 164-172. doi:10.1016/j.jclepro.2011.09.012.
52.
Hsu, C. W., Kuo, T. C., Shyu, G. S., & Chen, P. S. (2014). Low Carbon Supplier Selection in the Hotel Industry. Sustainability, 6(5), 2658-2684. doi:10.3390/su6052658.
53.
Huang, C.-Y., & Kao, Y.-S. (2015). UTAUT2 Based Predictions of Factors Influencing the Technology Acceptance of Phablets by DNP. Mathematical Problems in Engineering, 501, 603747. doi:10.1155/2015/603747.
54.
International Data Corporation (IDC). (2009). Worldwide marketplace model and forecast. Framingham, MA.
55.
Järveläinen, J. (2013). IT incidents and business impacts: Validating a framework for continuity management in information systems. International Journal of Information Management, 33(3), 583-590. doi:10.1016/j.ijinfomgt.2013.03.001.
56.
Judson, J. (2012). Disaster Recovery Best Practices - Templates, Documents and Examples of Disaster Recovery in the Public Domain PLUS access to content.theartofservice.com for downloading: Emereo.
57.
Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2013, 7-10 Jan. 2013). Big Data: Issues and Challenges Moving Forward. Paper presented at the System Sciences (HICSS), 2013 46th Hawaii International Conference on.
58.
Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data analytics. Journal of Parallel and Distributed Computing, 74(7), 2561-2573. doi:10.1016/j.jpdc.2014.01.003.
59.
Katal, A., Wazid, M., & Goudar, R. (2013). Big data: issues, challenges, tools and good practices. Paper presented at the Contemporary Computing (IC3), 2013 Sixth International Conference on. doi:10.1109/IC3.2013.6612229.
60.
Kiernan, V. (2005a). Disasters may have wide ripple effects. Chronicle of Higher Education, 52(8), 32.
61.
Kiernan, V. (2005b). Ready for the next Katrina. Chronicle of Higher Education, 52(8), A31-A33.
62.
Kshetri, N. (2014). Big data׳ s impact on privacy, security and consumer welfare. Telecommunications Policy, 38(11), 1134-1145. doi:10.1016/j.telpol.2014.10.002.
63.
Khoshkholghi, M. A., Abdullah, A., Latip, R., Subramaniam, S., & Othman, M. (2014). Disaster recovery in cloud computing: A survey. Computer and Information Science, 7(4), 39. doi:10.5539/cis.v7n4p39.
64.
Lam, J., Ng, K. K., Cheung, S. K., Wong, T. L., Li, K. C., & Wang, F. L. (2015). Technology in Education. Technology-Mediated Proactive Learning: Second International Conference, ICTE 2015, Hong Kong, China, July 2-4, 2015, Revised Selected Papers (Vol. 559): Springer. doi:10.1007/978-3-662-48978-9.
65.
Lampa, S., Dahlö, M., Olason, P. I., Hagberg, J., & Spjuth, O. (2013). Lessons learned from implementing a national infrastructure in Sweden for storage and analysis of next-generation sequencing data. Gigascience, 2(1), 1. doi:10.1186/2047-217X-2-9.
66.
Lee, V. R. (November-December, 2013). The Quantified Self (QS) movement and some emerging opportunities for the educational technology field. Educational Technology, 39.
67.
Li, C.-W., & Tzeng, G.-H. (2009). Identification of a threshold value for the DEMATEL method using the maximum mean de-entropy algorithm to find critical services provided by a semiconductor intellectual property mall. Expert Systems with Applications, 36(6), 9891-9898. doi:10.1016/j.eswa.2009.01.073.
68.
Liao, S., Wu, M.-J., Huang, C.-Y., Kao, Y.-S., & Lee, T.-H. (2014). Evaluating and Enhancing Three-Dimensional Printing Service Providers for Rapid Prototyping Using the DEMATEL Based Network Process and VIKOR. Mathematical Problems in Engineering, 1-16. doi:10.1155/2014/349348.
70.
Lin, L., Shuang, W., Yifang, L., & Shouyang, W. (2014). A New Idea of Study on the Influence Factors of Companies’ Debt Costs in the Big Data Era. Procedia Computer Science, 31, 532-541. doi:10.1016/j.procs.2014.05.299.
71.
Linstone, H. A., & Turoff, M. (2002). The Delphi Method. Techniques and applications, 53.
72.
Liu, H. C., You, J. X., Zhen, L., & Fan, X. J. (2014). A novel hybrid multiple criteria decision making model for material selection with target-based criteria. Materials & Design, 60, 380-390. doi:10.1016/j.matdes.2014.03.071.
73.
Lu, M. T., Lin, S. W., & Tzeng, G. H. (2013). Improving RFID adoption in Taiwan's healthcare industry based on a DEMATEL technique with a hybrid MCDM model. [Article]. Decision Support Systems, 56, 259-269. doi:10.1016/j.dss.2013.06.006.
74.
Lyon, D. (2014). Surveillance, Snowden, and big data: Capacities, consequences, critique. Big Data & Society, 1(2), doi:10.1177/2053951714541861.
75.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. http://www.mckinsey.com/busine....
76.
Mattingly, K. D., Rice, M. C., & Berge, Z. L. (2012). Learning analytics as a tool for closing the assessment loop in higher education. Knowledge Management & E-Learning: An International Journal (KM&EL), 4(3), 236-247.
77.
Mayer-Schönberger, V., & Cukier, K. (2014). Big data: A revolution that will transform how we live, work, and think: Houghton Mifflin Harcourt.
78.
Mayer, B. W., Moss, J., & Dale, K. (2008). Disaster and preparedness: lessons from Hurricane Rita. Journal of Contingencies and Crisis Management, 16(1), 14-23. doi:10.1111/j.1468-5973.2008.00531.x.
79.
Minelli, M., Chambers, M., & Dhiraj, A. (2012). Big data, big analytics: emerging business intelligence and analytic trends for today's businesses: John Wiley & Sons.
80.
Miyagawa, M. (2012). Joint distribution of distances to the first and the second nearest facilities. Journal of geographical systems, 14(2), 209-222. doi:10.1007/s10109-010-0143-3.
81.
Murry, J. W., & Hammons, J. O. (1995). Delphi: A versatile methodology for conducting qualitative research. The Review of Higher Education, 18(4), 423. doi:10.1353/rhe.1995.0008.
82.
Naranjo-Gil, D. (2009). Management information systems and strategic performances: The role of top team composition. International Journal of Information Management, 29(2), 104-110. doi:10.1016/j.ijinfomgt.2008.05.009.
83.
Opricovic, S. (1998). Multicriteria optimization of civil engineering systems. Faculty of Civil Engineering, Belgrade, 2(1), 5-21.
84.
Opricovic, S., & Tzeng, G.-H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445-455. doi:10.1016/S0377-2217(03)00020-1.
85.
Ou Yang, Y.-P., Shieh, H.-M., Leu, J.-D., & Tzeng, G.-H. (2008). A novel hybrid MCDM model combined with DEMATEL and ANP with applications. International Journal of Operations Research, 5(3), 160-168.
86.
Papamitsiou, Z. K., & Economides, A. A. (2014). Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence. Educational Technology & Society, 17(4), 49-64.
87.
Pirkul, H., & Schilling, D. (1989). The capacitated maximal covering location problem with backup service. Annals of Operations Research, 18(1), 141-154. doi:10.1007/BF02097800.
88.
Rabbany, R., Takaffoli, M., & Zaïane, O. R. (2011). Analyzing participation of students in online courses using social network analysis techniques. Paper presented at the Proceedings of educational data mining.
89.
Rodger, J. A., Bhatt, G., Chaudhary, P., Kline, G., & McCloy, W. (2015). The Impact of Business Expertise on Information System Data and Analytics Resilience (ISDAR) for Disaster Recovery and Business Continuity: An Exploratory Study. Intelligent Information Management, 7(04), 223. doi:10.4236/iim.2015.74017.
90.
Roebuck, K. (2012). Business continuity and disaster recovery: High-impact Technology - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors: Emereo Publishing.
91.
Romero, C., & Ventura, S. (2010). Educational data mining: a review of the state of the art. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 40(6), 601-618.
92.
Rothstein, P. J. (2007). Disaster Recovery Testing: Exercising Your Contingency Plan (2007 Edition): Rothstein Associates Incorporated.
93.
Saaty, T. L. (1999). Decision making for leaders: the analytic hierarchy process for decisions in a complex world (Vol. 2): RWS publications.
94.
Sahebjamnia, N., Torabi, S. A., & Mansouri, S. A. (2015). Integrated business continuity and disaster recovery planning: Towards organizational resilience. European Journal of Operational Research, 242(1), 261-273. doi:10.1016/j.ejor.2014.09.055.
95.
Sembiring, J., & Siregar, M. I. H. (2013). A Decision Model for IT Risk Management on Disaster Recovery Center in an Enterprise Architecture Model. Procedia Technology, 11, 1142-1146. doi:10.1016/j.protcy.2013.12.306.
96.
Sengupta, S., & Annervaz, K. (2014). Multi-site data distribution for disaster recovery—A planning framework. Future Generation Computer Systems, 41, 53-64. DOI:10.1016/j.future.2014.07.007.
97.
Serrelis, E., & Alexandris, N. (2006). Disaster Recovery Sites as a Tool of Managing Extreme Attacks. Paper presented at the International Conference on Internet Surveillance and Protection (ICISP 06). doi:10.1109/ICISP.2006.9.
98.
Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE review, 46(5), 30.
99.
Smith, G. (2012). Planning for Post-Disaster Recovery: A Review of the United States Disaster Assistance Framework: Island Press.
100.
Snedaker, S. (2013). Business continuity and disaster recovery planning for IT professionals: Newnes.
101.
Sutton, J., & Tierney, K. (2006). Disaster preparedness: concepts, guidance, and research. Paper presented at the Fritz Institute Assessing Disaster Preparedness Conference.
102.
Tammineedi, R. L. (2010). Business continuity management: A standards-based approach. Information Security Journal: A Global Perspective, 19(1), 36-50. doi:10.1080/19393550903551843.
103.
TechAdvisory.org. (2010). Disaster Recovery: An Increasingly Important Aspect of Your Business, from http://www.techadvisory.org/20....
104.
Thejendra, B. (2014). Disaster Recovery and Business Continuity: A quick guide for small organisations and busy executives: IT Governance Publishing.
105.
Tian, W., & Zhao, Y. (2015). Big data technologies and cloud computing. Optimized Cloud Resource Management and Scheduling, Morgan Kaufmann, Boston, 17-49.
106.
Tierney, K. J. (2007). From the margins to the mainstream? Disaster research at the crossroads. Sociology, 33(1), 503. doi:10.1146/annurev.soc.33.040406.131743.
107.
Tzeng, G.-H., & Huang, C.-Y. (2012). Combined DEMATEL technique with hybrid MCDM methods for creating the aspired intelligent global manufacturing & logistics systems. Annals of Operations Research, 197(1), 159-190. doi:10.1007/s10479-010-0829-4.
108.
Villars, R. L., Olofson, C. W., & Eastwood, M. (2011). Big data: What it is and why you should care. White Paper, IDC.
109.
Wallace, M., & Webber, L. (2010). The disaster recovery handbook: A step-by-step plan to ensure business continuity and protect vital operations, facilities, and assets: AMACOM Div American Mgmt Assn.
110.
Wallace, M., Webber, L., & Webber, L. (2011). The Disaster Recovery Handbook: A Step-by-step Plan to Ensure Business Continuity and Protect Vital Operations, Facilities, and Assets: AMACOM.
111.
Wang, N., & Pin, L. (2008). Research on the Information Resources Management Center Construction in E-Government. Paper presented at the 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.
112.
Wang, Y.-L., & Tzeng, G.-H. (2012). Brand marketing for creating brand value based on a MCDM model combining DEMATEL with ANP and VIKOR methods. Expert systems with Applications, 39(5), 5600-5615. doi:10.1016/j.eswa.2011.11.057.
113.
Webb, G. R., Tierney, K. J., & Dahlhamer, J. M. (2000). Businesses and disasters: Empirical patterns and unanswered questions. Natural Hazards Review, 1(2), 83-90. doi:10.1061/(ASCE)1527-6988(2000)1:2(83).
114.
Whitson, G. (2003). Computer security: theory, process and management. Journal of computing sciences in colleges, 18(6), 57-66. doi:10.1108/09685220310500153.
115.
Wiboonrat, M. (2008). An empirical IT contingency planning model for disaster recovery strategy selection. Paper presented at the 2008 IEEE International Engineering Management Conference.
116.
Yang, C.-L., Yuan, B. J., & Huang, C.-Y. (2015). Key Determinant derivations for information technology disaster recovery site selection by the multi-criterion decision making method. Sustainability, 7(5), 6149-6188. doi:10.3390/su7056149.
117.
Yang, Y.-P. O., Shieh, H.-M., & Tzeng, G.-H. (2013). A VIKOR technique based on DEMATEL and ANP for information security risk control assessment. Information Sciences, 232, 482-500. doi:10.1016/j.ins.2011.09.012.
118.
Yang, Y., Li, W., & Yuan, D. (2014). Reliability Assurance of Big Data in the Cloud: Cost-effective Replication-based Storage: Morgan Kaufmann.
119.
Zhang, M. (2014). Who are interested in online science simulations? Tracking a trend of digital divide in Internet use. Computers & Education, 76, 205-214. doi:10.1016/j.compedu.2014.04.001.
120.
Zhang, N., Yan, Y., Xu, S., & Su, W. (2014). A distributed data storage and processing framework for next-generation residential distribution systems. Electric Power Systems Research, 116, 174-181. doi:10.1016/j.epsr.2014.06.005.
121.
Zhou, Q., Huang, W., & Zhang, Y. (2011). Identifying critical success factors in emergency management using a fuzzy DEMATEL method. Safety Science, 49(2), 243-252. doi:10.1016/j.ssci.2010.08.005.
122.
Zikopoulos, P. C., Eaton, C., DeRoos, D., Deutsch, T., & Lapis, G. (2012). Understanding big data. New York et al: McGraw-Hill.
Share
RELATED ARTICLE
| eISSN: | 1305-8223 |
| ISSN: | 1305-8215 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
